{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt, font_manager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4394029 7860119 5845909 ...  142463 2162240  515000]\n",
      " [ 320053  185853  576597 ...    4231   41032   34727]\n",
      " [   5931   26679   39774 ...     148    1384     195]\n",
      " [  46245       0  170708 ...     279    4737    4722]]\n",
      "****************************************************************************************************\n",
      "[[4394029  320053    5931   46245]\n",
      " [7860119  185853   26679       0]\n",
      " [5845909  576597   39774  170708]\n",
      " ...\n",
      " [ 142463    4231     148     279]\n",
      " [2162240   41032    1384    4737]\n",
      " [ 515000   34727     195    4722]]\n",
      "****************************************************************************************************\n"
     ]
    }
   ],
   "source": [
    "us_file_path = r'E:\\pythonProject\\day54\\youtube_video_data\\US_video_data_numbers.csv'\n",
    "uk_file_path = r'E:\\pythonProject\\day54\\youtube_video_data\\GB_video_data_numbers.csv'\n",
    "# delimiter是分隔符，一般是unpack为false，一列为一个特征\n",
    "t1 = np.loadtxt(us_file_path, delimiter=\",\", dtype=\"int\", unpack=True)\n",
    "t2 = np.loadtxt(us_file_path, delimiter=\",\", dtype=\"int\")\n",
    "\n",
    "print(t1)\n",
    "print(\"*\" * 100)\n",
    "print(t2)\n",
    "print(\"*\" * 100)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1688, 4)\n",
      "(1688,)\n"
     ]
    }
   ],
   "source": [
    "t_us = t2\n",
    "# 取评论的数据\n",
    "t_us_comments = t_us[:, -1]\n",
    "print(t_us.shape)\n",
    "print(t_us_comments.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "(1373,)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_us_comments = t_us_comments[t_us_comments <= 5000]\n",
    "t_us_comments.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4995 0\n"
     ]
    }
   ],
   "source": [
    "print(t_us_comments.max(), t_us_comments.min())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = 50\n",
    "bin_nums = (t_us_comments.max() - t_us_comments.min()) // d\n",
    "\n",
    "# 绘图\n",
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "\n",
    "plt.hist(t_us_comments, bin_nums)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_uk = np.loadtxt(uk_file_path, delimiter=\",\", dtype=\"int\")\n",
    "\n",
    "#选择喜欢数比50万小的数据\n",
    "t_uk = t_uk[t_uk[:, 1] <= 500000]\n",
    "\n",
    "t_uk_comment = t_uk[:, -1]\n",
    "t_uk_like = t_uk[:, 1]\n",
    "\n",
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "plt.scatter(t_uk_like, t_uk_comment)\n",
    "\n",
    "# 调整x轴的刻度\n",
    "my_font = font_manager.FontProperties(fname='C:\\Windows\\Fonts\\STSONG.TTF', size=10)\n",
    "\n",
    "plt.xlabel(' 好评数量 ', fontproperties=my_font)\n",
    "plt.ylabel(' 评论数量 ', fontproperties=my_font)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "(1688, 2)"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据\n",
    "us_file_path = r'E:\\pythonProject\\day54\\youtube_video_data\\USvideos1.csv'\n",
    "\n",
    "#有字符串就读取不了，usecols读了以后选那几列\n",
    "t_us = np.loadtxt(us_file_path, delimiter=\",\", usecols=(1, 2), skiprows=True, encoding='utf8')\n",
    "t_us.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}